which shows that the entries for the covariance function are low when the inputs are far away from each other and close to \(\sigma _f^2\)  when they are close to each other. In addition, the \(\sigma _f^2\)  and \(\ell \)  are the so-called hyperparameters, respectively the signal variance and length scale, that are optimized based on data using marginal likelihood optimization [1]}.
